STATS 218: Statistical Analysis of Networks

2020-10-03

This course is a introduction to the analysis of social structure, conceived in terms of social relationships. Major concepts of social network theory and mathematical representation of social concepts such as role and position. Use of graphical representations of network information.

A detailed description of the class is available here.

Motivation and Synopsis

This course is an introduction to the analysis of social structure, conceived in terms of social relationships.

Social structure is conceptualized as a system of social relations tieing distinct social entities to one another. Social network theory is the attempt to represent the structure in social relations via networks. It is then a theory pertaining to types of observable social spaces and their relation to individual and group behavior.

Observations on the social relations are of two forms:

  • individual level information on the social entities
  • relational data on pairs of entities

While both forms are important for the study of social relations, social network theory recognizes fundamental role of the relational information. It is based on the premise that social context is an important determinant of individual behavior. It seeks to understand individual and group behavior in terms of relational information rather than as solely the aggregation of individual characteristics.

The focus of the course are modern methods for the statistical analysis of social networks. The course covers the major concepts of social network theory and the mathematical representation of social concepts such as “role” and “position”.

Visualization plays a central in social network analysis. With the development of high speed computing and graphical display tools, visualization has become a flexible and powerful tool in the exploration of social relations. Graphics exploit the power of our visual senses to convey information in a direct way. In this course we will emphasize the use of graphical representations of network information as much as possible.

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